Book Image

Learning Jupyter

By : Dan Toomey
Book Image

Learning Jupyter

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Table of Contents (16 chapters)
Learning Jupyter
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Log file examination


I downloaded one of the access_log files from http://www.monitorware.com/. Like any other web access log, we have one line per entry, like this:

64.242.88.10 - - [07/Mar/2004:16:05:49 -0800] "GET /twiki/bin/edit/Main/Double_bounce_sender?topicparent=Main.ConfigurationVariables HTTP/1.1" 401 12846
  • The first part is the IP address of the caller, followed by timestamp, type of HTTP access, URL referenced, HTTP type, resultant HTTP Response code, and finally, the number of bytes in the response.

  • We can use Spark to load in and parse out some statistics of the log entries, as in this script:

import pyspark
if not 'sc' in globals():
    sc = pyspark.SparkContext()
textFile = sc.textFile("access_log")
print(textFile.count(),"access records")
gets = textFile.filter(lambda line: "GET" in line)
print(gets.count(),"GETs")
posts = textFile.filter(lambda line: "POST" in line)
print(posts.count(),"POSTs")
other = textFile.subtract(gets).subtract(posts)
print(other.count(),"Other")...